format_data: Format data for pscrub and CompCor
In neuroconductor/fMRIscrub: Scrubbing and Other Data Cleaning Routines for fMRI

Format data for pscrub and CompCor

format_data(
  X,
  ROI_data = "infer",
  ROI_noise = NULL,
  noise_nPC = 5,
  noise_erosion = NULL
)

`X`	Wide numeric data matrix (T observations by V variables, T << V). For example, if `X` represents an fMRI run, T should be the number of timepoints and V should be the number of brainordinate vertices/voxels. Or, a 4D array or NIFTI or file path to a NIFTI (I by J by K by T observations), in which case `ROI_data` must be provided. (The vectorized data will be T timepoints by V_{in-mask} voxels) Or, a `ciftiTools` `"xifti"` object or a file path to a CIFTI (The vectorized data will be T timepoints by V_{left+right+sub} greyordinates).
`ROI_data`	Indicates the data ROI. Allowed arguments depend on `X`: If `X` is a matrix, this must be a length V logical vector, where the data ROI is indicated by `TRUE` values. If `"infer"` (default), all columns of `X` will be included in the data ROI (`rep(TRUE, V)`). If `X` is an array or NIFTI, this must be either a vector of values to expect for out-of-mask voxels in `X`, or a (file path to a) 3D NIFTI. In the latter case, each of the volume dimensions should match the first three dimensions of `X`. Voxels in the data ROI should be indicated by `TRUE` and all other voxels by `FALSE`. If `"infer"` (default), will be set to `c(0, NA, NaN)` (include all voxels which are not constant `0`, `NA`, or `NaN`). If `X` is a `"xifti"` this must be the `brainstructures` argument to `read_cifti`. If `"infer"` (default), `brainstructures` will be set to `"all"` (use both left and right cortex vertices, and subcortical voxels). If `NULL`, the data ROI will be empty. This is useful for obtaining just the noise ROI, if the data and noise are located in separate files.
`ROI_noise`	Indicates the noise ROIs for aCompCor. Should be a list where each entry corresponds to a distinct noise ROI. The names of the list should be the ROI names, e.g. `"white_matter"` and `"csf"`. The expected formats of the list entries depends on `X`: For all types of `X`, `ROI_noise` entries can be a matrix of noise ROI data. The matrix should have T rows, with each column being a data location's timeseries. If `X` is a matrix, entries can also indicate a noise ROI within `X`. These entries must be a length V logical vector with `TRUE` values indicating locations in `X` within that noise ROI. Since the ROIs must not overlap, the masks must be mutually exclusive with each other, and with `ROI_data`. If `X` is an array or NIFTI, entries can also indicate a noise ROI within `X`. These entries must be a logical array or (file path to) a 3D NIFTI with the same spatial dimensions as `X`, and with `TRUE` values indicating voxels inside the noise ROI. Since the ROIs must not overlap, the masks must be mutually exclusive with each other, and with `ROI_data`. (If `X` is a `"xifti"`, entries must be data matrices, since no greyordinate locations in `X` are appropriate noise ROIs).
`noise_nPC`	The number of principal components to compute for each noise ROI. Alternatively, values between 0 and 1, in which case they will represent the minimum proportion of variance explained by the PCs used for each noise ROI. The smallest number of PCs will be used to achieve this proportion of variance explained. Should be a list or numeric vector with the same length as `ROI_noise`. It will be matched to each ROI based on the name of each entry, or if the names are missing, the order of entries. If it is an unnamed vector, its elements will be recycled. Default: `5` (compute the top 5 PCs for each noise ROI).
`noise_erosion`	The number of voxel layers to erode the noise ROIs by. Should be a list or numeric vector with the same length as `ROI_noise`. It will be matched to each ROI based on the name of each entry, or if the names are missing, the order of entries. If it is an unnamed vector, its elements will be recycled. Default: `NULL`, which will use a value of 0 (do not erode the noise ROIs).